Every month, finance teams at banks wade through the same ritual: reconcile the ledgers, cross-check the numbers, fill out the forms, brace for examiner questions. Regulatory reporting is nobody's idea of stimulating work. It's dozens of hours spent hunting for discrepancies, matching balances, and documenting exceptions—labor that feels Sisyphean because, well, it comes around again in thirty days.
So when AI agents arrive promising to automate the drudgery, skepticism is warranted. Sure, a model can spit out numbers. But will regulators accept them? Will auditors sign off? Will anyone actually trust a black box to file reports that could trigger enforcement actions?
Definite, a startup fresh out of Y Combinator's Summer 2026 batch, has a theory: trust doesn't come from the AI alone. It comes from what happens after the AI does its work.
The company launched around mid-July with a platform that pairs AI agents with a separate verification engine. The agents pull data, assemble filings, crunch the numbers. Then the verification layer recalculates everything independently—from source ledgers, against a versioned rulebook—before anything reaches a regulator. The founders call it "compiler errors for financial agents." The idea is to turn weeks of regulatory grunt work into hours, complete with receipts for the auditors.
It's an audacious pitch from a team that, by all appearances, is still operating out of a shared apartment or co-working space. No publicly named customers yet. No public pilots. Just a product site, a launch post asking for introductions to bank CFOs, and a conviction that the industry's about to need exactly what they built.
Whether they're right depends on questions far bigger than their headcount.
Verification as a Wedge
Here's what Definite actually does. The platform connects—read-only—to core banking systems, general ledgers, payroll providers, accounting software. NetSuite, QuickBooks, Sage, Workday, ADP, Stripe, Bill.com, Ramp. It syncs nightly, copying data but never writing back. The AI agents ingest that data and assemble the regulatory filing packages that banks submit to federal or state watchdogs.
Then comes the unusual part. Instead of handing the output straight to compliance officers, Definite runs a second pass through what it calls a verification engine. This separate layer independently recalculates each figure from the ledger, checks it against regulatory definitions stored in a rulebook, and flags discrepancies. If a number doesn't match, the system routes it back to the agent with an explanation. Wrong outputs don't make it through.
Every calculation gets a tamper-evident receipt. Change a number post-verification and the chain breaks—auditors and examiners can see someone intervened. It's provenance borrowed from blockchain thinking, applied to regulatory arithmetic.
The company also built an Excel add-in, which might be the smartest distribution move. Finance teams live in spreadsheets. Definite surfaces verified balances and variances directly in cells, receipts embedded, meeting people where they already work rather than asking them to abandon legacy workflows.
Definite claims its approach—using DeepSeek V3 with the verification layer—outperformed GPT-5 on reconciliation accuracy in a benchmark called FinBalance, at roughly one-fiftieth the cost. It also says it beat competitors on XBRL calculation validation in another benchmark, FinAuditing. These claims have not been independently validated.
Who's Behind It
The founders are recent graduates from the University of Waterloo, a school with a track record of producing technical talent that Wall Street and Silicon Valley tend to notice. Gurshabd Singh Varaich, the CEO, worked at BitGo on wallet policy and approval controls—experience in environments where trust and verification aren't optional. Mazin Al-Ani interned at Optiver and Boosted.ai, building quant systems. Farhan Ur Rehman spent time at Meta working on Instagram Reels recommendations, which is, admittedly, a very different problem from regulatory compliance.
Their framing of the opportunity is practical, almost understated. Banks face recurring deadlines. Data sprawls across fragmented systems. Exceptions require human triage and drafted explanations for regulators. The gap, as they see it, is between what generative AI can do and what finance teams can actually deploy in a regulated environment without inviting disaster.
The founders position the verification engine as their answer to that gap. Whether it's the right answer—or whether banks will trust a YC startup to touch filings that land on an examiner's desk—remains very much an open question.
A Crowded, Fast-Moving Field
Timing is tricky here. Definite is small, but the market is enormous and moving fast, perhaps faster than anyone anticipated even six months ago.
In May, Fiserv launched agentOS, a marketplace for third-party AI agents handling regulatory reporting, risk management, and reconciliation, all built on AWS Bedrock. Broadridge announced agentic AI deployments across capital markets operations the same month, with controls designed to satisfy regulators. In June, FIS partnered with Anthropic on a financial crimes AI agent, signaling a broader agent-first strategy for regulated work.
The incumbents aren't sitting still either. Regnology rolled out an agentic AI layer in its Ascend platform in late March. DFIN introduced AI-powered iXBRL tagging for SEC filings in June. Suade, a regulatory reporting vendor, positioned itself as "agentic AI-ready" in April. NeoXam launched agents for investment operations in late June, with compliance officers certifying agent fleets for regulators—a detail that sounds almost surreal until you remember this is the world we're in now.
Behind all this product activity is a regulatory landscape that's tightening, not loosening. The European Banking Authority issued enhanced operational risk reporting guidance ahead of a June reference date. The Federal Reserve and FDIC have proposed compliance standards for payment stablecoin issuers. The European Commission published transparency guidelines for AI systems under the EU AI Act in July. In February, the U.S. Treasury released a financial-sector AI lexicon and risk management framework adapted from NIST standards.
In other words, the industry is awash in AI agents and new rules about how to use them responsibly. Definite's bet is that going deep on verification—rather than bolting AI onto legacy platforms—will matter in that environment.
The Open Questions
The startup has no disclosed customers. Its LinkedIn page lists somewhere between two and ten employees, though the YC profile shows three. That's a rounding error in a market where Workiva serves over 6,600 organizations and Fiserv, FIS, and Broadridge operate at planetary scale.
But scale isn't everything, especially early. A focused bet can find oxygen in a market where the giants are still figuring out their agent strategies. A late-July TechRadar Pro article discussed the operational and compliance risks of agentic AI in financial services—cost exposure, real-time risk, unbudgeted liabilities—suggesting the industry is still working through how to deploy these systems safely. HSBC and Google Cloud announced a partnership in June to codify regulatory procedures into AI structures. NIST launched an AI Agent Standards Initiative in February.
The market is clearly moving. Standards are forming, or trying to. Definite's read-only architecture and rulebook-driven verification, as the founders propose, could be well-suited for this moment.
Or banks might decide they'd rather buy agent capabilities from vendors they already trust, companies with existing contracts and established compliance relationships. A three-person YC startup asking to touch regulatory filings is, let's be honest, a hard sell. The founders know this—hence the launch post asking for introductions.
What Definite has is a thesis: that verification, not generation, is where the trust problem gets solved. That the provenance of a number matters as much as the number itself. That finance teams will adopt AI agents faster if there's a second system double-checking the first.
It's a reasonable theory. Whether it's a fundable company, scalable go-to-market motion, and defensible business is what the next twelve months will reveal. The founders are betting that when banks finally do pull the trigger on agentic regulatory reporting, they'll want receipts—literally. And that the market will reward the team that figured out how to generate them.
